This proposal will bring together a team with world-class expertise in ophthalmic disease, imaging and biomedical engineering to synthesize information from photographs and scanning laser ophthalmoscope (SLO) images to improve our understanding of age-related macular degeneration (AMD), the leading cause of blindness in the developed world. As we develop the technology to analyze these images, we will reach out to share and test our methods with internationally recognized centers for AMD research. We propose to develop accurate, cost-effective, automatic digital image analysis tools that are more efficient than the present manual methods. Cost considerations are particularly important in the increasingly strained health-care environment. For example, the cost for manual image analysis alone in an important NEI trial, the Age Related Eye Disease Study, was about $5.5M. Automated methods also lend themselves to streamlined sharing between institutions for further economies of scale and cost. The diagnosis and treatment of AMD are based on photography of the macula, hence the paramount importance of the information in these images. For example, the subretinal deposits known as drusen are the hallmark of this disease. Researchers have been trying for two decades, with limited results, to use digital techniques to quantify drusen. A major difficulty in identifying drusen is that the background in which they are embedded is inherently non-uniform. We have developed prototype automated mathematical models of the macula with sufficient power to overcome this obstacle and accurately identify drusen in the majority of images encountered in less than a minute. With sophisticated neural networks, level set deformable images, and other image engineering techniques from our colleagues in biomedical engineering that have proved effective for analysis of MRI images and mammograms, we propose to extend our capability to all macular images. One concept has been key to our efforts so far: leveling image background by a mathematical model for uniform object identification. This breakthrough may have wider significance: the ideas of computing such a model from partial background image data, and using it to remove background variability from the image, could be useful for identifying pathological structures in other types of medical images. The newer imaging technique of scanning laser ophthalmoscopy (SLO) can provide information about the biochemical basis of AMD, and in infrared mode, image subretinal structures. The SLO measures autofluorescence (AF), which reveals the accumulation of a potentially toxic substance called lipofuscin in the aging eye. The build-up of lipofuscin may be at least in part genetically determined. The gene known as ABCR definitely causes build-up in patients with juvenile macular degeneration and has been implicated in AMD. Linking this gene, or other candidate genes, with adult MD is the goal of our sister study, the Columbia Macular Genetics Study (CMGS). During the first three years we have been able to generate a clinical database of over 2,200 study subjects, and expect to reach our target of 3600 subjects in two more years, with macular photographs, SLO images and DNA samples from each subject. We propose to synthesize a wealth of information from the photographs and SLO images (AF and infrared) of the CMGS by combining our automated analyses of individual images with image registration of the three image types. For example, we have already analyzed AF images in registration with drusen photographs to provide evidence for a dramatic shift (from 75 percent to 20 percent) in the co-localization of drusen and hyperfluorescence from stage 3 (drusen) to stage 4 (drusen and geographic atrophy) AMD that may provide new insight on the natural history. In collaboration with King's College Hospital, London, we will pursue validation and understanding of these findings, which is now a major goal of this application. This is the type of work that could provide the linkage of specific DNA mutations to specific AMD image characteristics. Such knowledge could form the basis for early diagnosis of individuals at risk, who could then receive specific therapies based on specific molecular defects. These advances would extend profound health and social benefits to our aging population.